Composite energy storage system regulation and control operation method and system considering energy rebalance

Abstract

The invention relates to the technical field of energy storage regulation and control, in particular to a compound energy storage system regulation and control operation method and system considering energy rebalance. Obtaining a plurality of state parameter time sequences of each energy storage unit in the composite energy storage system, an instruction execution life cycle and power grid electric power parameter time sequence data when the instruction is successfully executed; the state parameter time sequence data of the energy storage unit is compared with expected comparison data to analyze the difference, a state consistency index is obtained, and the actual and ideal state deviation can be quantified; analyzing response delay based on the instruction execution life cycle data, and determining an instruction execution stability index of the energy storage unit in combination with a power grid power parameter discrete fluctuation state when the instruction is executed; the state consistency index and the instruction execution stability index are fused to determine the operation stability index, the index comprehensively reflects the comprehensive operation state of the energy storage unit, fine regulation and control are facilitated, the advantages of different energy storage units are brought into play, and the overall performance and the operation efficiency of the system are improved.

Description

Technical Field

[0001] This invention relates to the field of energy storage regulation technology, specifically to a method and system for regulating and operating a composite energy storage system that considers energy rebalancing. Background Technology

[0002] With the continued growth of global energy demand and the increasing emphasis on clean energy utilization, energy storage systems are becoming increasingly widespread and crucial in the energy sector. As a comprehensive energy storage solution integrating the advantages of multiple energy storage technologies, hybrid energy storage systems can combine the characteristics of different energy storage units to exhibit superior performance in power regulation, energy storage, and release, thus meeting the complex and ever-changing demands of the electricity market.

[0003] The core of a composite energy storage system for energy rebalancing is to achieve real-time balance between energy supply and demand within the power grid. Existing technologies typically rely on power allocation strategies to regulate energy storage units. However, in actual operation, different energy storage devices have different technical characteristics, and the grid status and the command execution status of the energy storage units will have a certain impact on the actual operation of the energy storage units. Therefore, if we rely solely on power allocation strategies without a comprehensive assessment of the overall operating status of the energy storage units, it will be difficult to fully leverage the advantages of composite energy storage systems. Summary of the Invention

[0004] To address the problem that existing technologies for regulating energy storage units typically rely solely on power allocation strategies, which are insufficient in practice due to varying technical characteristics of different energy storage devices and the influence of grid conditions and command execution status on the actual operation of the energy storage units, this invention aims to provide a method and system for regulating and operating a composite energy storage system that considers energy rebalancing. The specific technical solution adopted is as follows: A method for regulating and operating a composite energy storage system considering energy rebalancing, the method comprising: In a composite energy storage system, time-series data of various state parameters of each energy storage unit are acquired, life-cycle data of instruction execution of each energy storage unit is acquired, and time-series data of various power parameters of the power grid are acquired when each energy storage unit successfully executes the instruction. The time-series data of the state parameters of each energy storage unit are compared with the corresponding expected comparison data. The differences are analyzed to obtain the state consistency index of each energy storage unit. Based on the instruction execution lifecycle data analysis of each energy storage unit, the instruction execution status and response delay are analyzed, and combined with the discrete fluctuation status of the power grid's power parameter time series data when each energy storage unit executes instructions, the instruction execution stability index of each energy storage unit is determined. By integrating the state consistency index of each energy storage unit with the command execution stability index, the operational stability index of each energy storage unit is determined, and the operation of the energy storage unit is regulated based on the operational stability index of each energy storage unit.

[0005] Furthermore, the method for obtaining the state consistency index includes: The state parameters include SOC and power; Based on the EMS management system, the predicted SOC time series data of each energy storage unit is obtained, the difference characteristics between the SOC time series data of each energy storage unit and the corresponding predicted SOC time series data are calculated, and the SOC expected consistency factor of each energy storage unit is determined. The deviation characteristics between the power value in the power time series data of each energy storage unit and the preset expected power are calculated to obtain the power efficiency factor of each energy storage unit; The normalized sum of the SOC expected consistency factor and the power efficiency factor of each energy storage unit is used as the state consistency index of each energy storage unit.

[0006] Furthermore, the method for obtaining the SOC expected consistency factor includes: In the SOC time series data and the corresponding predicted SOC time series data of each energy storage unit, the absolute value of the difference between the SOC values ​​at the same time is used as the SOC deviation factor at each time. The sum of the SOC deviation factors at all times is negatively correlated and normalized, and the resulting value is used as the SOC expected consistency factor for each energy storage unit.

[0007] Furthermore, the method for obtaining the power effective factor includes: Based on the power time-series data of each energy storage unit, the absolute value of the difference between the power value at each moment and the preset expected power at each moment is used as the power deviation factor at each moment. The ratio of the power deviation factor at each moment to the rated power of each energy storage unit is used as the power deviation characteristic value at each moment. The sum of the power deviation eigenvalues ​​at all times is negatively correlated and normalized, and the resulting value is used as the power effective factor for each energy storage unit.

[0008] Furthermore, the method for obtaining the instruction execution stability index includes: Based on the instruction execution lifecycle data of each energy storage unit, the instruction response latency is analyzed to determine the instruction execution effectiveness value of each energy storage unit; For each energy storage unit, the discrete fluctuation state of the power parameter time series data when the command is successfully executed is analyzed to obtain the execution stability factor of each energy storage unit under each successfully executed command; The normalized value is the product of the mean of the execution stability factor of each energy storage unit under all successfully executed instructions and the instruction execution effectiveness value of each energy storage unit. This product is used as the instruction execution stability index of each energy storage unit.

[0009] Furthermore, the method for obtaining the instruction execution effectiveness value includes: The instruction execution lifecycle data includes the instruction issuance time and the instruction execution time; In the lifetime data of all instructions executed by each energy storage unit, the ratio of the number of successfully executed instructions to the total number of instructions is used as the instruction completion rate of each energy storage unit. For each successfully executed instruction in each energy storage unit, the time difference between the instruction execution time and the instruction issuance time is calculated as the response time. The sum of the response times of all successfully executed instructions in each energy storage unit is negatively correlated and normalized to become the instruction execution response index. The normalized sum of the instruction completion rate and instruction execution response index for each energy storage unit is used as the instruction execution effectiveness value for each energy storage unit.

[0010] Furthermore, the method for obtaining the execution stability factor includes: In the time-series data of each power parameter of the power grid under each successfully executed instruction of each energy storage unit, the difference between the maximum and minimum values ​​is taken as the fluctuation amplitude of each power parameter. Calculate the coefficient of variation of time series data for each power parameter, perform negative correlation mapping on the product of the coefficient of variation and the fluctuation amplitude for each power parameter, and normalize the result to use the change stability factor for each power parameter. The sum of the stability factors of all power parameters of each energy storage unit under each successfully executed instruction is used as the execution stability factor of each energy storage unit under each successfully executed instruction.

[0011] Furthermore, the method for obtaining the operational stability indicators includes: The normalized sum of the state consistency index and instruction execution index of each energy storage unit is used as the operational stability index of each energy storage unit.

[0012] Furthermore, the regulation and operation of the energy storage unit includes: The types of energy storage units include flywheel energy storage units, lithium battery energy storage units, and lead-carbon battery energy storage units. At the current moment, obtain the maximum adjustable power value of each energy storage unit, multiply the ratio of the maximum adjustable power value of each energy storage unit to the rated power of each energy storage unit by the operating stability index of each energy storage unit, and use the normalized value of the product as the selection priority of each energy storage unit. The power demand signal and target power are obtained based on the EMS management system; the power demand signal is subjected to spectrum analysis to obtain a spectrum diagram; the high frequency band, mid frequency band and low frequency band are determined according to the frequency value in the spectrum diagram; and the power demand value corresponding to each frequency band is obtained based on the EMS management system. When the ratio of the power demand value corresponding to the high frequency band to the target power is less than a preset ratio, the flywheel energy storage units with a selection priority greater than the preset first threshold and the lithium battery energy storage units with a selection priority greater than the preset second threshold are arranged in descending order of selection priority and used as the control units for the high frequency band in sequence according to the arrangement order; the lithium battery energy storage units of the control units in the non-high frequency band are used as the control units for the mid frequency band in descending order of selection priority, and the lead-carbon battery energy storage units with a selection priority greater than the preset third threshold are used as the control units for the low frequency band in descending order of selection priority; When the ratio of the power demand value corresponding to the high-frequency band to the target power is greater than the preset ratio, all flywheel energy storage units are arranged in descending order of selection priority, and thus used as control units for the high-frequency band in sequence; all lithium battery energy storage units are arranged in descending order of selection priority, and thus used as control units for the mid-frequency band in sequence; all lead-carbon energy storage batteries are arranged in descending order of selection priority, and thus used as control units for the low-frequency band in sequence.

[0013] A control and operation system for a composite energy storage system that considers energy rebalancing includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the control and operation method for the composite energy storage system that considers energy rebalancing.

[0014] The present invention has the following beneficial effects: Acquiring time-series data of various state parameters, command execution lifecycle data, and time-series data of various power parameters of the power grid when commands are successfully executed for each energy storage unit in a hybrid energy storage system provides rich and comprehensive data support for subsequent accurate assessment and control of the energy storage unit's operating status. When the actual operating status of an energy storage unit is relatively consistent with its ideal operating status, it can be considered that the energy storage unit has good operational stability, and its priority in subsequent control processes will be higher. Therefore, the time-series data of the state parameters of each energy storage unit are compared with the corresponding expected comparison data, and the differences are analyzed to obtain a state consistency index. This index can accurately quantify the deviation between the actual operating status and the ideal state of each energy storage unit. Furthermore, in addition to analyzing the consistency between its actual operating status and the ideal state, it is also necessary to measure the reliability of the energy storage unit's command execution in order to measure the operational stability of the energy storage unit. Therefore, the command response delay is analyzed based on the command execution lifecycle data, and the discrete fluctuation state of the power grid's power parameter time-series data when commands are executed is combined to determine the command execution stability index. This index comprehensively considers the characteristics of the energy storage unit itself and the impact of the power grid state on command execution, and can comprehensively and accurately assess the stability of the energy storage unit during command execution. Finally, the operational stability index is determined by integrating the state consistency index corresponding to the energy storage unit with the command execution stability index. This comprehensive operational stability index can fully reflect the overall operational status of the energy storage unit, which is conducive to achieving refined control of the composite energy storage system and giving full play to the advantages of different energy storage units. Therefore, controlling the operation of energy storage units based on this index can greatly improve the overall performance and operating efficiency of the composite energy storage system and better meet the complex and ever-changing power market demands. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating a method for regulating and operating a composite energy storage system that considers energy rebalancing, provided in an embodiment of the present invention. Figure 2 A flowchart illustrating a method for obtaining a state consistency index according to an embodiment of the present invention; Figure 3 A flowchart illustrating a method for obtaining instruction execution stability indicators according to an embodiment of the present invention; Figure 4This is a system block diagram of a composite energy storage system control and operation system considering energy rebalancing, provided in one embodiment of the present invention. Figure 5 This is a schematic diagram of the system structure of a composite energy storage system control and operation system that considers energy rebalancing, provided as an embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a composite energy storage system control and operation method and system considering energy rebalancing proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] The following description, in conjunction with the accompanying drawings, details a specific scheme for the regulation and operation method and system of a composite energy storage system that considers energy rebalancing, provided by the present invention.

[0020] Please see Figure 1 The diagram illustrates a method flowchart for regulating and operating a composite energy storage system considering energy rebalancing, according to an embodiment of the present invention. The method includes the following steps: Step S1: In the composite energy storage system, acquire the timing data of various state parameters of each energy storage unit, acquire the instruction execution lifecycle data of each energy storage unit, and acquire the timing data of various power parameters of the power grid when each energy storage unit successfully executes the instruction.

[0021] The composite energy storage system consists of various energy storage units. In this embodiment of the invention, three types are used as examples: flywheel energy storage units, lithium battery energy storage units, and lead-carbon battery energy storage units, with multiple units of each type. By configuring energy storage converters, SOC monitoring modules, and sensors, various state parameters and time-series data of each energy storage unit are collected in real time. These state parameters include SOC and power.

[0022] Meanwhile, the composite energy storage system is equipped with a grid interface unit to execute the charging and discharging commands of each energy storage unit. According to the system log, the command execution lifecycle data of each energy storage unit can be obtained. The command execution lifecycle data covers the entire process information of issuing the command, the energy storage system executing the command, and the feedback result. In this embodiment of the invention, the command issuance time and the command execution time of the energy storage unit are mainly extracted. In addition, a high-precision power quality analyzer is configured in the grid interface unit to obtain the time series data of various power parameters of the grid when the energy storage unit successfully executes the command. The types of power parameters include voltage, current, etc.

[0023] It should be noted that, in this embodiment of the invention, the acquisition frequency of various time series data is set to 25Hz; the time period for all data acquisition is set to the historical 8 hours starting from the current time. The specific time period and acquisition frequency settings can be adjusted according to the implementation scenario, and are not limited here.

[0024] The integrated energy storage system also includes a local EMS (energy management subsystem) to achieve model predictive control, droop control, and SOC balancing.

[0025] Step S2: Compare the time-series data of the state parameters of each energy storage unit with the corresponding expected comparison data, analyze the differences, and obtain the state consistency index of each energy storage unit.

[0026] In practical applications, composite energy storage systems may face different operating conditions and demands. The expected comparison data is set based on the design goals and expected operating states of the composite energy storage system. These goals include the rated capacity, charge and discharge efficiency, and power output range of the energy storage units. Therefore, the expected comparison data reflects the performance indicators that the energy storage units should achieve under ideal conditions. By comparing it with the time series data of the state parameters of the energy storage units during actual operation, it can be determined whether the energy storage units are operating normally according to the expected requirements, thereby obtaining the state consistency index of each energy storage unit, which serves as a reference for characterizing the operational stability of the energy storage units.

[0027] Preferably, in one embodiment of the present invention, the method for obtaining the state consistency index includes: Please see Figure 2 The diagram illustrates a method flowchart for obtaining a state consistency index according to an embodiment of the present invention. The method includes the following steps: Step S201: Based on the EMS management system, obtain the predicted SOC time series data of each energy storage unit, calculate the difference characteristics between the SOC time series data of each energy storage unit and the corresponding predicted SOC time series data, and determine the SOC expected consistency factor of each energy storage unit.

[0028] The EMS (Energy Management System) can predict the SOC (State of Charge) of each energy storage unit based on the system's operating status, historical data, and preset control strategies, generating predicted SOC time-series data (expected comparison data corresponding to the SOC state parameter). This data provides a benchmark for subsequent comparison with actual SOC time-series data, helping to assess whether the actual changes in the SOC of the energy storage unit meet expectations.

[0029] Therefore, the predicted SOC time series data of each energy storage unit in the past historical period is first obtained based on the EMS management system. Then, the absolute value of the difference between the SOC time series data and the corresponding predicted SOC time series data of each energy storage unit is used as the SOC deviation factor at each time. The larger the SOC deviation factor, the greater the difference between the actual state and the ideal state under the SOC state parameter, and the lower the operating reliability of the energy storage unit at that time.

[0030] Then, the sum of the SOC deviation factors at all times is calculated. Based on the aforementioned logic, the larger the sum, the more significant the gap between the actual state and the ideal state, and the lower the degree of consistency with expectations. Therefore, a negative correlation mapping and normalization process is applied to this sum to correct the logical relationship and obtain the SOC expected consistency factor for each energy storage unit. The larger the SOC expected consistency factor, the more the operating state of the energy storage unit, represented by the SOC state parameter, conforms to the expected state within the historical period, and is considered to have higher operational stability or reliability. The negative correlation mapping and normalization process here can be performed using the formula... in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0031] Step S202: Calculate the deviation characteristics between the power value in the power time series data of each energy storage unit and the preset expected power to obtain the power effective factor of each energy storage unit.

[0032] In the power time-series data of each energy storage unit, the absolute value of the difference between the power value at each moment and the preset expected power at each moment (which can be obtained from the EMS management system and represents the expected power value to be achieved during the regulation process, that is, the expected comparison data corresponding to the power state parameter) is used as the power deviation factor at each moment. The larger the power deviation factor, the greater the difference between the actual state and the ideal state under the power state parameter, and the lower the operational reliability of the energy storage unit at that moment.

[0033] Rated power is an important parameter of an energy storage unit, reflecting its maximum power output capability. Therefore, by calculating the ratio of the power deviation factor to the rated power of each energy storage unit at each moment, the influence of differences in energy storage unit specifications can be eliminated, making the power deviation of different energy storage units comparable. This ratio is used as the power deviation characteristic value corresponding to each moment. The larger the power deviation characteristic value, the greater the deviation between the power state of the energy storage unit at that moment and the expected state, and the lower the operational reliability of the energy storage unit.

[0034] Finally, the sum of power deviation eigenvalues ​​at all times is calculated. Based on the aforementioned logic, the smaller the sum, the closer the actual state is to the ideal state, indicating a higher degree of power efficiency and better consistency with expectations. Therefore, a negative correlation mapping and normalization process is applied to this sum to correct the logical relationship and obtain the power efficiency factor for each energy storage unit. A larger power efficiency factor indicates that the operating state of the energy storage unit, represented by its power as a state parameter, is more consistent with the expected state over historical periods, signifying higher operational stability or reliability. This negative correlation mapping and normalization process can be performed using the formula... in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0035] Step S203: Combine the SOC expected consistency factor and power efficiency factor of each energy storage unit to obtain the state consistency index of each energy storage unit.

[0036] Based on the analysis in steps S201 and S202, it is known that the SOC expected consistency factor and power efficiency factor of the energy storage unit are both positively correlated with the consistency between the actual state and the ideal expected state of the energy storage unit. Therefore, in this embodiment of the invention, in order to realize the aforementioned logical relationship, the sum of the SOC expected consistency factor and power efficiency factor of each energy storage unit can be calculated, and the sum can be normalized to obtain the state consistency index of each energy storage unit. At this time, the larger the state consistency index, the higher the operational reliability of the energy storage unit. Normalization is a well-known technique in the art, and the choice of normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0037] At this point, the state consistency index for each energy storage unit can be obtained.

[0038] Step S3: Analyze the instruction execution and response delay based on the instruction execution lifecycle data of each energy storage unit, and combine this with the discrete fluctuation state of the power grid's power parameter time series data when each energy storage unit executes instructions to determine the instruction execution stability index of each energy storage unit.

[0039] To measure the operational stability of each energy storage unit, in addition to considering the consistency between the actual and ideal states, it is also necessary to measure the reliability of the unit's execution of charge and discharge control commands. The reliability of the energy storage unit's execution of control commands indicates the reliability of the unit's execution process, reflecting the possibility of anomalies occurring during command execution, and thus demonstrating the unit's stability. The reliability of the energy storage unit's execution of control commands can be characterized by its command execution status, response delay, and the discrete fluctuations of the grid's power parameter time-series data during command execution. Therefore, in this embodiment of the invention, the command execution stability index of each energy storage unit is calculated using the aforementioned data characteristics.

[0040] Preferably, in one embodiment of the present invention, the method for obtaining the instruction execution stability index includes: Please see Figure 3 The diagram illustrates a method flowchart for obtaining instruction execution stability indicators according to an embodiment of the present invention. The method includes the following steps: Step S301: Based on the instruction execution lifecycle data of each energy storage unit, analyze the instruction response latency and determine the instruction execution effectiveness value of each energy storage unit.

[0041] Instruction execution lifecycle data includes the instruction issuance time and the instruction execution time.

[0042] Command completion is a fundamental and critical indicator. If an energy storage unit cannot successfully execute a large number of commands, its overall command execution capability will inevitably be poor. Therefore, in the lifecycle data of all commands executed by each energy storage unit, the ratio of the number of successfully executed commands to the total number of commands is used as the command completion rate of each energy storage unit. The lower the command completion rate, the more serious the problem is in the command receiving, processing or execution stage of the energy storage unit, and the lower the operational stability will be. Therefore, its participation rate should be minimized in the subsequent regulation and operation process.

[0043] Furthermore, in practical applications, rapid command response is crucial for maintaining system stability and meeting grid requirements. Therefore, for each successfully executed command, its response delay can be analyzed: for each successfully executed command of each energy storage unit, the time difference between the command execution time and the command issuance time is calculated as the response duration. The longer the response duration, the longer the response time, meaning the execution action is not completed in a timely manner. Therefore, the sum of the response durations of all successfully executed commands for each energy storage unit is negatively correlated and normalized to correct the logical relationship, resulting in a command execution response index. A larger command execution response index indicates higher command execution efficiency for that energy storage unit, thus contributing more to maintaining grid stability. This negative correlation mapping and normalization can be performed using the formula... in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0044] Based on the foregoing analysis, it is known that the command completion rate and command execution response index corresponding to each energy storage unit are positively correlated with the effectiveness of command execution of the energy storage unit. Therefore, the sum of the command completion rate and command execution response index corresponding to each energy storage unit was calculated, and the normalized value of this sum was used as the command execution effectiveness value of each energy storage unit. Based on the foregoing analysis, the larger the command execution effectiveness value, the more timely the response of the energy storage system to commands during operation, and thus the higher its stability and contribution to the power grid. Normalization is a technique well-known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0045] Step S302: Under each energy storage unit, analyze the discrete fluctuation state of the power parameter time series data when the command is successfully executed, and obtain the execution stability factor of each energy storage unit under each successfully executed command.

[0046] After determining the effective value of command execution for each energy storage unit, the operational stability of the energy storage unit can be determined by analyzing the fluctuations in the power parameters of the power grid during each command execution.

[0047] First, in the time-series data of each power parameter of the power grid under each successfully executed command of each energy storage unit, the difference between the maximum and minimum values ​​is taken as the fluctuation amplitude of each power parameter. The fluctuation amplitude can intuitively reflect the range of change of the power parameter during the execution of the command by the energy storage unit. The larger the value, the greater the fluctuation and the lower the stability.

[0048] Then, given that the coefficient of variation can reflect the dispersion of data, the coefficient of variation for each power parameter's time-series data is calculated. A larger coefficient of variation indicates more pronounced fluctuations in that power parameter during the energy storage unit's execution of commands, and similarly, poorer stability. Therefore, the product of the coefficient of variation and the fluctuation amplitude for each power parameter is negatively correlated and normalized to correct the logical relationship, thereby obtaining the stability factor for each power parameter. This negative correlation mapping and normalization can be performed using the formula... in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0049] Thus, we can obtain the change stability factor for each power parameter of each energy storage unit under each successfully executed instruction. The smaller the value, the worse the stability of the power parameter is considered. This also indicates that the energy storage unit is less stable during instruction execution. Therefore, the sum of the change stability factors of all power parameters of each energy storage unit under each successfully executed instruction is taken as the execution stability factor of each energy storage unit under each successfully executed instruction. Based on the above logic, it can be seen that the larger the execution stability factor, the better the execution effect of the energy storage unit under the successfully executed instruction.

[0050] Step S303: Combine the execution stability factor of each energy storage unit under all successfully executed instructions with the instruction execution effectiveness value of each energy storage unit to obtain the instruction execution stability index of each energy storage unit.

[0051] Based on the logic in steps S301 and S302, it is known that the execution stability factor and the instruction effectiveness value are positively correlated with the instruction execution effect of the energy storage unit. Therefore, the mean of the execution stability factor of each energy storage unit under all successfully executed instructions is multiplied by the instruction execution effectiveness value of each energy storage unit to realize the aforementioned positive correlation. The normalized value of the resulting product is used as the instruction execution stability index of each energy storage unit. The larger the instruction execution stability index, the higher the instruction execution reliability of the energy storage unit and the higher its contribution to the stability maintenance of the power grid. Therefore, its participation should be increased in subsequent regulation and operation. Normalization is a well-known technique in the art. The normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0052] Step S4: Integrate the state consistency index of each energy storage unit with the command execution stability index to determine the operation stability index of each energy storage unit, and regulate the operation of the energy storage unit based on the operation stability index of each energy storage unit.

[0053] By integrating state consistency indicators and command execution stability indicators for each energy storage unit, operational stability indicators can be determined, enabling a comprehensive and accurate assessment of the operational status of each unit from multiple key dimensions. State consistency indicators reflect the degree to which the unit's state parameters match the expected or ideal state, while command execution stability indicators demonstrate the unit's response and execution capabilities to external commands. Integrating these two indicators avoids the limitations of single-indicator assessments and provides a more accurate understanding of the actual operational status of the energy storage unit. Different energy storage units, due to their unique characteristics and operating environments, will have varying operational stability indicators. Therefore, based on each unit's unique operational stability indicators, personalized control strategies can be developed for each unit when grid regulation is required. Personalized control and operation can fully leverage the advantages of each unit, improving its operational efficiency and reliability, and extending its service life.

[0054] Preferably, in one embodiment of the present invention, the method for obtaining operational stability indicators includes: Based on the calculations in steps S2 and S3, it is known that the larger the state consistency index of an energy storage unit, the higher its operational reliability and thus its operational stability. Similarly, a larger command execution stability index indicates higher command execution reliability, which can also be considered as greater operational stability. Therefore, both the state consistency index and the command execution stability index of an energy storage unit are positively correlated with its operational stability index. Thus, the normalized sum of the state consistency index and command execution index for each energy storage unit is used as its operational stability index. A larger operational stability index indicates a higher contribution of the energy storage unit to the stable maintenance of the power grid, and therefore its participation should be increased in subsequent regulation and operation. Normalization is a well-known technique in the field, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0055] After obtaining the operational stability indicators of each energy storage unit in the composite energy storage system, for energy storage units with higher operational stability indicators, given their relatively stable operation, they can be assigned to take on more important tasks or higher loads. Therefore, in this embodiment of the invention, the operation of energy storage units can be regulated based on their operational stability indicators.

[0056] Preferably, in one embodiment of the present invention, the process of regulating and operating the energy storage unit includes: At the current moment (the moment when energy storage units need to regulate the stability of the power grid), the maximum adjustable power value of each energy storage unit is obtained. The ratio of the maximum adjustable power value of each energy storage unit to its rated power is calculated. This ratio reflects the proportion of power that each energy storage unit can provide to its rated power at the current moment. The larger the value, the shallower its power output or absorption capacity, and the more suitable it is for participating in regulation tasks. This ratio is multiplied by the operating stability index of each energy storage unit, and the normalized value of the product is used as the selection priority of each energy storage unit. The higher the selection priority, the stronger the energy storage unit's ability to participate in regulation in terms of both power capacity and operating stability at the current moment, and therefore the higher the selection priority. Normalization is a technique well known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0057] The energy storage units in this embodiment of the invention include flywheel energy storage units, lithium battery energy storage units, and lead-carbon battery energy storage units. Different energy storage units have different strengths. Flywheel energy storage units have a response speed of milliseconds, making them suitable for high-frequency control. Lithium battery energy storage units have a response speed of seconds, making them suitable for mid-frequency control. Of course, they can also control both high and low frequencies. Lead-carbon battery energy storage units, with a response speed of minutes, are more suitable for low-frequency control.

[0058] At the current moment, the power demand signal and target power (the desired power to be adjusted) are obtained based on the EMS management system. In practical applications, power demand is often complex and variable, containing components of different frequencies. Therefore, Fourier transform is used to perform spectral analysis on the power demand signal to obtain a spectrum diagram. Based on the frequency values ​​in the spectrum diagram, high-frequency bands, mid-frequency bands, and low-frequency bands are determined, and the power demand values ​​corresponding to each frequency band are obtained based on the EMS management system. In this embodiment of the invention, in the obtained spectrum diagram, at all frequencies where amplitude exists, the first third of the frequency band is designated as the low-frequency band, the second third as the high-frequency band, and the remaining portion as the mid-frequency band.

[0059] When the ratio of the power demand value corresponding to the high-frequency band to the target power is less than the preset ratio, it indicates that the high-frequency power demand is relatively small. Therefore, it is not necessary for all flywheel energy storage units to participate in high-frequency regulation. Thus, flywheel energy storage units with a selection priority greater than the preset first threshold and lithium battery energy storage units with a selection priority greater than the preset second threshold are arranged in descending order of selection priority and used as high-frequency band regulation units in that order. Then, the remaining lithium battery energy storage units that are not high-frequency band regulation units are also arranged in descending order of selection priority as mid-frequency band regulation units, and lead-carbon battery energy storage units with a selection priority greater than the preset third threshold are arranged in descending order of selection priority as low-frequency band regulation units.

[0060] When the ratio of the power demand for the high-frequency band to the target power exceeds a preset ratio, it indicates a large high-frequency power demand. Therefore, as many energy storage units as possible need to participate in high-frequency regulation. Thus, all flywheel energy storage units are arranged in descending order of selection priority, and then used as high-frequency regulation units in that order. Similarly, all lithium battery energy storage units are arranged in descending order of selection priority, and then used as mid-frequency regulation units in that order. Likewise, all lead-carbon energy storage batteries are arranged in descending order of selection priority, and then used as low-frequency regulation units in that order. This ensures that energy storage units with strong power adjustability and stable operation are prioritized to maximize the fulfillment of high-frequency power demands.

[0061] It should be noted that obtaining the spectrum using Fourier transform is a well-known technique, and the specific process will not be described in detail here. In this embodiment of the present invention, the preset ratio is set to 0.7, the preset first threshold is set to 0.8, the preset second threshold is set to 0.85, and the preset third threshold is set to 0.65. The specific values ​​can be calculated based on experience in historical control processes, or can be adjusted according to the implementation scenario, and are not limited here.

[0062] In this embodiment of the invention, all numerical values ​​involved in the calculation have undergone data preprocessing to eliminate the influence of dimensions. The specific means of eliminating the influence of dimensions are well known to those skilled in the art and will not be limited or described in detail here.

[0063] In summary, acquiring time-series data of various state parameters, command execution lifecycle data, and time-series data of various power parameters of the power grid when commands are successfully executed for each energy storage unit in the composite energy storage system provides rich and comprehensive data support for the subsequent accurate assessment and control of the energy storage unit's operating status. When the actual operating status of an energy storage unit is relatively consistent with its ideal operating status, it can be considered that the energy storage unit has good operational stability, and thus will be given higher priority in subsequent control processes. Therefore, by comparing the time-series data of the state parameters of each energy storage unit with the corresponding expected comparison data and analyzing the differences, a state consistency index is obtained. This index can accurately quantify the deviation between the actual operating status and the ideal state of each energy storage unit. Furthermore, in addition to analyzing the consistency between its actual operating status and the ideal state, it is also necessary to measure the reliability of the energy storage unit's command execution in order to measure the operational stability of the energy storage unit. Therefore, based on the command execution lifecycle data analysis of command response delay and combined with the discrete fluctuation state of the power grid's power parameter time-series data when commands are executed, a command execution stability index is determined. This index comprehensively considers the characteristics of the energy storage unit itself and the impact of the power grid state on command execution, and can comprehensively and accurately assess the stability of the energy storage unit during command execution. Finally, the operational stability index is determined by integrating the state consistency index corresponding to the energy storage unit with the command execution stability index. This comprehensive operational stability index can fully reflect the overall operational status of the energy storage unit, which is conducive to achieving refined control of the composite energy storage system and giving full play to the advantages of different energy storage units. Therefore, controlling the operation of energy storage units based on this index can greatly improve the overall performance and operating efficiency of the composite energy storage system and better meet the complex and ever-changing power market demands.

[0064] This invention also provides a control and operation system for a composite energy storage system that considers energy rebalancing. Please refer to [link / reference]. Figure 4 The diagram shows a system block diagram, including a data acquisition module 401 for implementing step S1 in the above method embodiment; a state change analysis module 402 for implementing step S2 in the above method embodiment; an instruction execution analysis module 403 for implementing step S3 in the above method embodiment; and a operation control module 404 for implementing step S4 in the above method embodiment.

[0065] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the embodiment of a composite energy storage system control and operation system considering energy rebalancing and the embodiment of a composite energy storage system control and operation method considering energy rebalancing provided above belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0066] Please see Figure 5 This illustration shows a schematic diagram of the system structure of a composite energy storage system control and operation system considering energy rebalancing, provided by an embodiment of the present invention. It includes a processor 500, a memory 501, a bus 502, and a communication interface 503. The processor 500, communication interface 503, and memory 501 are connected via the bus 502. The memory 501 may include a high-speed random access memory, and the bus 502 may be an ISA bus, PCI bus, or EISA bus, etc. The processor 500 may be an integrated circuit chip with signal processing capabilities. The memory 501 stores at least one instruction, at least one program, code set, or instruction set. When the processor loads and executes the at least one instruction, at least one program, code set, or instruction set, it implements the steps in a method for controlling and operating a composite energy storage system considering energy rebalancing.

[0067] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0068] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for regulating and operating a composite energy storage system considering energy rebalancing, characterized in that, The method includes: In a composite energy storage system, time-series data of various state parameters of each energy storage unit are acquired, life-cycle data of instruction execution of each energy storage unit is acquired, and time-series data of various power parameters of the power grid are acquired when each energy storage unit successfully executes the instruction. The time-series data of the state parameters of each energy storage unit are compared with the corresponding expected comparison data. The differences are analyzed to obtain the state consistency index of each energy storage unit. Based on the instruction execution lifecycle data analysis of each energy storage unit, the instruction execution status and response delay are analyzed, and combined with the discrete fluctuation status of the power grid's power parameter time series data when each energy storage unit executes instructions, the instruction execution stability index of each energy storage unit is determined. By integrating the state consistency index of each energy storage unit with the command execution stability index, the operational stability index of each energy storage unit is determined, and the operation of the energy storage unit is regulated based on the operational stability index of each energy storage unit.

2. The method for regulating and operating a composite energy storage system considering energy rebalancing according to claim 1, characterized in that, The method for obtaining the state consistency index includes: The state parameters include SOC and power; Based on the EMS management system, the predicted SOC time series data of each energy storage unit is obtained, the difference characteristics between the SOC time series data of each energy storage unit and the corresponding predicted SOC time series data are calculated, and the SOC expected consistency factor of each energy storage unit is determined. The deviation characteristics between the power value in the power time series data of each energy storage unit and the preset expected power are calculated to obtain the power efficiency factor of each energy storage unit; The normalized sum of the SOC expected consistency factor and the power efficiency factor of each energy storage unit is used as the state consistency index of each energy storage unit.

3. The method for regulating and operating a composite energy storage system considering energy rebalancing according to claim 2, characterized in that, The method for obtaining the SOC expected consistency factor includes: In the SOC time series data and the corresponding predicted SOC time series data of each energy storage unit, the absolute value of the difference between the SOC values ​​at the same time is used as the SOC deviation factor at each time. The sum of the SOC deviation factors at all times is negatively correlated and normalized, and the resulting value is used as the SOC expected consistency factor for each energy storage unit.

4. The method for regulating and operating a composite energy storage system considering energy rebalancing according to claim 2, characterized in that, The method for obtaining the power effective factor includes: Based on the power time-series data of each energy storage unit, the absolute value of the difference between the power value at each moment and the preset expected power at each moment is used as the power deviation factor at each moment. The ratio of the power deviation factor at each moment to the rated power of each energy storage unit is used as the power deviation characteristic value at each moment. The sum of the power deviation eigenvalues ​​at all times is negatively correlated and normalized, and the resulting value is used as the power effective factor for each energy storage unit.

5. The method for regulating and operating a composite energy storage system considering energy rebalancing according to claim 1, characterized in that, The method for obtaining the instruction execution stability index includes: Based on the instruction execution lifecycle data of each energy storage unit, the instruction response latency is analyzed to determine the instruction execution effectiveness value of each energy storage unit; For each energy storage unit, the discrete fluctuation state of the power parameter time series data when the command is successfully executed is analyzed to obtain the execution stability factor of each energy storage unit under each successfully executed command; The normalized value is the product of the mean of the execution stability factor of each energy storage unit under all successfully executed instructions and the instruction execution effectiveness value of each energy storage unit. This product is used as the instruction execution stability index of each energy storage unit.

6. The method for regulating and operating a composite energy storage system considering energy rebalancing according to claim 5, characterized in that, The method for obtaining the instruction execution effectiveness value includes: The instruction execution lifecycle data includes the instruction issuance time and the instruction execution time; In the lifetime data of all instructions executed by each energy storage unit, the ratio of the number of successfully executed instructions to the total number of instructions is used as the instruction completion rate of each energy storage unit. For each successfully executed instruction in each energy storage unit, the time difference between the instruction execution time and the instruction issuance time is calculated as the response time. The sum of the response times of all successfully executed instructions in each energy storage unit is negatively correlated and normalized to serve as the instruction execution response index. The normalized sum of the instruction completion rate and instruction execution response index for each energy storage unit is used as the instruction execution effectiveness value for each energy storage unit.

7. The method for regulating and operating a composite energy storage system considering energy rebalancing according to claim 5, characterized in that, The method for obtaining the execution stability factor includes: In the time-series data of each power parameter of the power grid under each successfully executed instruction of each energy storage unit, the difference between the maximum and minimum values ​​is taken as the fluctuation amplitude of each power parameter. Calculate the coefficient of variation of time series data for each power parameter, perform negative correlation mapping on the product of the coefficient of variation and the fluctuation amplitude for each power parameter, and normalize the result to use the change stability factor for each power parameter. The sum of the stability factors of all power parameters of each energy storage unit under each successfully executed instruction is used as the execution stability factor of each energy storage unit under each successfully executed instruction.

8. The method for regulating and operating a composite energy storage system considering energy rebalancing according to claim 1, characterized in that, The methods for obtaining the operational stability indicators include: The normalized sum of the state consistency index and instruction execution index of each energy storage unit is used as the operational stability index of each energy storage unit.

9. The method for regulating and operating a composite energy storage system considering energy rebalancing according to claim 1, characterized in that, The regulation and operation of the energy storage unit includes: The types of energy storage units include flywheel energy storage units, lithium battery energy storage units, and lead-carbon battery energy storage units. At the current moment, obtain the maximum adjustable power value of each energy storage unit, multiply the ratio of the maximum adjustable power value of each energy storage unit to the rated power of each energy storage unit by the operating stability index of each energy storage unit, and use the normalized value of the product as the selection priority of each energy storage unit. The power demand signal and target power are obtained based on the EMS management system; the power demand signal is subjected to spectrum analysis to obtain a spectrum diagram; the high frequency band, mid frequency band and low frequency band are determined according to the frequency value in the spectrum diagram; and the power demand value corresponding to each frequency band is obtained based on the EMS management system. When the ratio of the power demand value corresponding to the high frequency band to the target power is less than a preset ratio, the flywheel energy storage units with a selection priority greater than the preset first threshold and the lithium battery energy storage units with a selection priority greater than the preset second threshold are arranged in descending order of selection priority and used as the control units for the high frequency band in sequence according to the arrangement order; the lithium battery energy storage units of the control units in the non-high frequency band are used as the control units for the mid frequency band in descending order of selection priority, and the lead-carbon battery energy storage units with a selection priority greater than the preset third threshold are used as the control units for the low frequency band in descending order of selection priority; When the ratio of the power demand value corresponding to the high-frequency band to the target power is greater than the preset ratio, all flywheel energy storage units are arranged in descending order of selection priority, and thus used as control units for the high-frequency band in sequence; all lithium battery energy storage units are arranged in descending order of selection priority, and thus used as control units for the mid-frequency band in sequence; all lead-carbon energy storage batteries are arranged in descending order of selection priority, and thus used as control units for the low-frequency band in sequence.

10. A control and operation system for a composite energy storage system considering energy rebalancing, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for regulating and operating a composite energy storage system that considers energy rebalancing as described in any one of claims 1 to 9.

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